Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues.

Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Research Abstract Details 

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Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Abstract Text:

Michael Mullins,Laurent Perreard,John F Quackenbush,Nicholas Gauthier,Steven Bayer,Matthew Ellis,Joel Parker,Charles M Perou,Aniko Szabo,Philip S Bernard,

BACKGROUND: Microarray studies have identified different molecular subtypes of breast cancer with prognostic significance. To transition these classifications into the clinical laboratory, we have developed a real-time quantitative reverse transcription (qRT)-PCR assay to diagnose the biological subtypes of breast cancer from fresh-frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissues. METHODS: We used microarray data from 124 breast samples as a training set for classifying tumors into 4 previously defined molecular subtypes: Luminal, HER2(+)/ER(-), basal-like, and normal-like. We used the training set data in 2 different centroid-based algorithms to predict sample class on 35 breast tumors (test set) procured as FF and FFPE tissues (70 samples). We classified samples on the basis of large and minimized gene sets. We used the minimized gene set in a real-time qRT-PCR assay to predict sample subtype from the FF and FFPE tissues. We evaluated primer set performance between procurement methods by use of several measures of agreement. RESULTS: The centroid-based algorithms were in complete agreement in classification from FFPE tissues by use of qRT-PCR and the minimized "intrinsic" gene set (40 classifiers). There was 94% (33 of 35) concordance between the diagnostic algorithms when comparing subtype classification from FF tissue by use of microarray (large and minimized gene set) and qRT-PCR data. We found that the ratio of the diagonal SD to the dynamic range was the best method for assessing agreement on a gene-by-gene basis. CONCLUSIONS: Centroid-based algorithms are robust classifiers for breast cancer subtype assignment across platforms and procurement conditions.

Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Publishing Authors By Initials

M Mullins,L Perreard,JF Quackenbush,N Gauthier,S Bayer,M Ellis,J Parker,CM Perou,A Szabo,PS Bernard,

For similar diagnosis: laboratory techniques and procedures: specimen handling research abstracts see: diagnosis: laboratory techniques and procedures: specimen handling research

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Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Clinical chemistry

VOLUME: 53

Page Numbers: 1273-9

Journal Abbreviation: Clin. Chem.

ISSN: 0009-9147

DAY: 24

MONTH: 05

YEAR: 2007

Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 9421549

Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Keywords Mesh Terms:

KEYWORDS: Specimen Handling

MESH TERMS: methods

Chemical & Substance for Abstract: Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Information

Substance Name: Formaldehyde

Registry Number: 50-00-0

Grant and Affiliation Information for Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues.

AFFILIATION: Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA.

Country: United States

AGENCY: United States NCI

GRANT: R33-CA97769-01

ACRONYM: CA

MEDLINETA: Clin Chem

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